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A common response here is that this looks at the long-term effects of AI safety but only the direct effects of global poverty work, when really you should look at the long-term effects of global poverty alleviation as well. Although this may make global poverty look worse because I believe the long-term effects look slightly more likely to be bad than good.
I feel like this post still doesn’t square the discrepancy between causes where empirical work is more or less viable, but I applaud getting more ‘harder’(?) evidence in any cause. The fact seems to be existential risk reduction won’t ever be amenable to the level of empirical evidence of other causes. We’re both familiar with Katja Grace’s seminal essay ‘estimation is the best we have’. I guess my response to this for some time has been “your estimates aren’t very good”, which is why I’m glad Grace is working at AI Impacts to get better ones.
I feel like there is a history of organizations in some causes claiming that the theoretical value of their work is so high that other considerations are moot, but they don’t make the case as to why their specific organization is effective. If, say, MIRI has the potential to be 15000x more effective than the best poverty intervention, I’m concerned as to why nobody has been trying to evaluate whether in practice the work MIRI is doing actually fulfills that potential. Lately, this has been changing, which keeps me hopeful.
There is a planned program to fund empirical research for animal advocacy https://www.animalcharityevaluators.org/blog/introducing-our-new-advocacy-research-program-officer/